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Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Feb;65(2 Pt 2):026137. Epub 2002 Jan 24.

Occam factors and model independent Bayesian learning of continuous distributions.

Author information

1
Department of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Abstract

Learning of a smooth but nonparametric probability density can be regularized using methods of quantum field theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the integration over the model space determine the free parameter of the theory ("smoothness scale") self-consistently. This persists even for distributions that are atypical in the prior and is a step towards a model independent theory for learning continuous distributions. Finally, we point out that a wrong parametrization of a model family may sometimes be advantageous for small data sets.

PMID:
11863617
DOI:
10.1103/PhysRevE.65.026137

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